Work

Tool-wielding language model-based agent offers conversational exploration of clinical tabular data

Year: 2026

Type: article

Abstract: Advancing evidence-based medicine requires integrating clinical expertise with data analysis. While clinicians contribute essential domain knowledge, applying modern data science methods often requires specialized training, creating a barrier to adoption. To bridge this gap, we developed ChatDA, an artificial intelligence agent enabling large-language-model-mediated conversational analysis of de-identified clinical tabular datasets. ChatDA empowers clinicians to extract meaningful insights efficiently and accurately, making data-driven clinical research more accessible and effective.

Source: npj Artificial Intelligence

DOI: 10.1038/s44387-025-00070-2

Authors: Andrew Yang, Joshua Woo, Ryan Zhang, Alan Mach, Prem Ramkumar, Ying Ma

Institutions: Brown University, The University of Texas at Austin

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,590